{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"![\"Open](\"https://colab.research.google.com/assets/colab-badge.svg\")
\n",
"\n",
"Uncomment the following line to install [geemap](https://geemap.org) if needed."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Mapping surface water dynamics using Earth Engine\n",
"\n",
"\n",
"Author: Qiusheng Wu ([Website](https://wetlands.io) - [GitHub](https://github.com/giswqs))\n",
"\n",
"**Keyboard shortcuts for Jupyter notebook:**\n",
"\n",
"- **Shift-Enter**: run cell, select below\n",
"- **Ctrl-Enter**: run selected cells\n",
"- **Alt-Enter**: run cell and insert below\n",
"- **Ctrl-/**: comment\n",
"- **Tab**: code completion or indent\n",
"- **Shift-Tab**: tooltip"
]
},
{
"cell_type": "markdown",
"metadata": {
"toc": true
},
"source": [
"Contents
\n",
""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Install geemap\n",
"\n",
"To follow this tutorial, you need to install the [geemap](https://github.com/gee-community/geemap) Python package.Uncomment the following line to install and update the package to the latest version (v0.7.3)."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# !pip install -U geemap"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Check package version."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import ee\n",
"import geemap"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print(ee.__version__)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print(geemap.__version__)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Get help\n",
"\n",
"- [Earth Engine API Documentation](https://developers.google.com/earth-engine/)\n",
"- [geeamp API Documentation](https://geemap.readthedocs.io/en/latest/source/geemap.html#geemap-package)\n",
"- [Report a geemap bug or submit a feature request](https://github.com/gee-community/geemap/issues)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"geemap.api_docs()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"geemap.open_youtube()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"geemap.ee_search()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Create an interactive map"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"Map = geemap.Map(center=[40, -100], zoom=4)\n",
"Map.add_basemap('HYBRID')\n",
"Map"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Create Landsat timelapse\n",
"\n",
"Use the Drawing tool to draw any rectangle on the map.\n",
"\n",
"https://earthengine.google.com/timelapse/\n",
"\n",
"https://geemap.readthedocs.io/en/latest/source/geemap.html#geemap.geemap.Map.add_landsat_ts_gif"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"Map.setCenter(-114.762293, 36.06462, 9)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"label = 'Surface water dynamics in Lake Mead'\n",
"Map.add_landsat_ts_gif(\n",
" label=label,\n",
" start_year=1985,\n",
" bands=['NIR', 'Red', 'Green'],\n",
" font_color='white',\n",
" frames_per_second=10,\n",
" progress_bar_color='blue',\n",
")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"Map.setCenter(-74.4557, -8.4289, 9)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"label = 'Surface Water Dynamics of Ucayali River, Peru'\n",
"Map.add_landsat_ts_gif(\n",
" label=label,\n",
" start_year=1985,\n",
" start_date='01-01',\n",
" end_date='12-31',\n",
" bands=['SWIR1', 'NIR', 'Red'],\n",
" font_color='white',\n",
" frames_per_second=10,\n",
" progress_bar_color='blue',\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Select the best cloud-free image\n",
"\n",
"In this case study, we can going to use [USGS Landsat 8 Surface Reflectance Tier 1](https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LC08_C01_T1_SR).\n",
"\n",
"- `ee.ImageCollection(\"LANDSAT/LC08/C01/T1_SR\")`"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"Map = geemap.Map()\n",
"Map.add_basemap('HYBRID')\n",
"Map.setCenter(-114.762293, 36.06462, 9)\n",
"Map"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Pan and zoom the map to Lake Mead near Las Vegas, NV. Use the Drawing Tools to place a marker inside Lake Mead."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"roi = Map.user_roi\n",
"print(roi.getInfo())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Alternatively, you can define an ee.Geometry() as an ROI."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"roi = ee.Geometry.Point([-114.762293, 36.06462])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's filter the ImageCollection by roi and date, and then sort by cloud cover."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"images = (\n",
" ee.ImageCollection('LANDSAT/LC08/C01/T1_SR')\n",
" .filterBounds(roi)\n",
" .filterDate('2015-01-01', '2019-12-31')\n",
" .sort('CLOUD_COVER')\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"How many Landsat 8 images (2015-2019) are available for the study area?"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print(images.size().getInfo())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Since the returned images are already sorted by CLOUD_COVER in ascending order, the first image would be the best cloud-free image."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# best_image = images.toList(images.size()).get(0)\n",
"best_image = images.first().select(['B1', 'B2', 'B3', 'B4', 'B5', 'B6', 'B7'])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's add the best image to the Map."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"vis_params = {\n",
" 'bands': ['B5', 'B4', 'B3'],\n",
" 'min': 0,\n",
" 'max': 6000,\n",
" 'gamma': 1.4,\n",
"}\n",
"Map.addLayer(best_image, vis_params, 'Best image')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Use the Inspector and Plotting to check pixel values and spectral signature."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Get image properties"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print(best_image.getInfo())"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print(best_image.propertyNames().getInfo())"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print(best_image.get('system:id').getInfo())"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"image = ee.Image('LANDSAT/LC08/C01/T1_SR/LC08_039035_20150724')\n",
"vis_params = {\n",
" 'bands': ['B6', 'B5', 'B4'],\n",
" 'min': 0,\n",
" 'max': 6000,\n",
" 'gamma': 1.4,\n",
"}\n",
"Map.addLayer(image, vis_params, 'image')\n",
"Map"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print(best_image.get('CLOUD_COVER').getInfo())"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print(best_image.get('system:band_names').getInfo())"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print(best_image.get('system:time_start').getInfo())"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print(ee.Date(best_image.get('system:time_start')).format('YYYY-MM-dd').getInfo())"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print(\n",
" \"WRS_Path={}, WRS_ROW={}\".format(\n",
" best_image.get('WRS_PATH').getInfo(), best_image.get('WRS_ROW').getInfo()\n",
" )\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Calculate NDWI\n",
"\n",
"The Normalized Difference Water Index (NDWI) is used to monitor changes related to water content in water bodies, using green and NIR wavelengths, defined by McFeeters (1996):\n",
"\n",
""
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"Map = geemap.Map()\n",
"Map"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"image = ee.Image('LANDSAT/LC08/C01/T1_SR/LC08_039035_20150724')\n",
"vis_params = {\n",
" 'bands': ['B5', 'B4', 'B3'],\n",
" 'min': 0,\n",
" 'max': 6000,\n",
" 'gamma': 1.4,\n",
"}\n",
"Map.addLayer(image, vis_params, 'image')\n",
"Map.centerObject(image, 8)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"ndwi_image = image.normalizedDifference(['B3', 'B5'])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"vis_ndwi = {\n",
" 'min': -1,\n",
" 'max': 1,\n",
" 'palette': [\n",
" '#ece7f2',\n",
" '#d0d1e6',\n",
" '#a6bddb',\n",
" '#74a9cf',\n",
" '#3690c0',\n",
" '#0570b0',\n",
" '#045a8d',\n",
" '#023858',\n",
" ],\n",
"}\n",
"Map.addLayer(ndwi_image, vis_ndwi, 'NDWI image')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Extract water"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"ndwi_threshold = 0\n",
"water_image = ndwi_image.gt(ndwi_threshold).selfMask()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"Map.addLayer(water_image, {'palette': 'blue'}, 'Water image')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"Map.layers"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"water_layer = Map.layers[-1]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"water_layer.interact(opacity=(0.0, 1.0, 0.1))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"Map"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Convert raster to vector"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"water_vector = water_image.reduceToVectors(scale=30, maxPixels=60000000)\n",
"Map.addLayer(water_vector, {}, 'Water vector')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"roi = ee.Geometry.Point([-114.762293, 36.06462])\n",
"lake_mead = water_vector.filterBounds(roi)\n",
"Map.addLayer(lake_mead, {}, 'Lake Mead')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"Map.addLayer(\n",
" ee.Image().paint(lake_mead, 0, 2), {'palette': 'blue'}, 'Lake Mead Outline'\n",
")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"area = lake_mead.geometry().area(1).divide(1e6).round().getInfo()\n",
"print(\"Area = {} km2\".format(area))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Apply the algorithm to all images"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"Map = geemap.Map()\n",
"Map"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Define input parameters"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"roi = ee.Geometry.Point([-114.762293, 36.06462])\n",
"start_date = '2015-01-01'\n",
"end_date = '2019-12-31'\n",
"cloud_threshold = 0.05\n",
"ndwi_threshold = 0"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"images = (\n",
" ee.ImageCollection('LANDSAT/LC08/C01/T1_SR')\n",
" .filterBounds(roi)\n",
" .filterDate(start_date, end_date)\n",
" .filterMetadata('CLOUD_COVER', 'less_than', cloud_threshold)\n",
")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print(images.size().getInfo())"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print(images.aggregate_array('system:id').getInfo())"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"dates = images.aggregate_array('system:time_start').map(\n",
" lambda d: ee.Date(d).format('YYYY-MM-dd')\n",
")\n",
"print(dates.getInfo())"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"first_image = ee.Image(images.toList(images.size()).get(3))\n",
"vis_params = {\n",
" 'bands': ['B5', 'B4', 'B3'],\n",
" 'min': 0,\n",
" 'max': 6000,\n",
" 'gamma': 1.4,\n",
"}\n",
"Map.addLayer(first_image, vis_params, 'First image')\n",
"Map.centerObject(roi, 8)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def extract_water(img):\n",
" ndwi_image = img.normalizedDifference(['B3', 'B5'])\n",
" water_image = ndwi_image.gt(ndwi_threshold)\n",
" return water_image"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"ndwi_images = images.map(extract_water)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"first_ndwi_image = ee.Image(ndwi_images.toList(ndwi_images.size()).get(0)).selfMask()\n",
"Map.addLayer(first_ndwi_image, {'palette': 'blue'}, 'First NDWI')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"occurrence = ndwi_images.sum().selfMask()\n",
"Map.addLayer(occurrence.randomVisualizer(), {}, 'Water occurrence')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def ras_to_vec(img):\n",
" vec = img.selfMask().reduceToVectors(scale=30, maxPixels=60000000)\n",
" vec = vec.filterBounds(roi)\n",
" return vec.set({'area': vec.geometry().area(1).divide(1e6).round()})"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"ndwi_vectors = ndwi_images.map(ras_to_vec)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"areas = ndwi_vectors.aggregate_array('area')\n",
"print(areas.getInfo())"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt\n",
"\n",
"%matplotlib inline\n",
"# %matplotlib qt\n",
"\n",
"fig = plt.figure(figsize=(12, 6))\n",
"\n",
"x = dates.getInfo()\n",
"y = areas.getInfo()\n",
"\n",
"plt.plot(x, y, marker='o')\n",
"plt.xlabel('Date')\n",
"plt.ylabel('Lake Mead Area (km2)')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Create a split-panel map to visualize results"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"water_images = ndwi_images.map(lambda img: img.selfMask())\n",
"landsat_images = images"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"water_layer_names = ['Water ' + str(date) for date in x]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"landsat_layer_names = ['Landsat ' + str(date) for date in x]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"water_vis = {'palette': 'blue'}\n",
"\n",
"landsat_vis = {\n",
" 'bands': ['B5', 'B4', 'B3'],\n",
" 'min': 0,\n",
" 'max': 6000,\n",
" 'gamma': 1.4,\n",
"}"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"Map = geemap.Map()\n",
"Map.ts_inspector(\n",
" left_ts=water_images,\n",
" right_ts=landsat_images,\n",
" left_names=water_layer_names,\n",
" right_names=landsat_layer_names,\n",
" left_vis=water_vis,\n",
" right_vis=landsat_vis,\n",
")\n",
"Map.centerObject(roi, zoom=8)\n",
"Map"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"layer_index = 9 # Change this index to your desired date\n",
"print(\"Image date: {}\".format(x[layer_index]))\n",
"\n",
"water_image = ee.Image(water_images.toList(water_images.size()).get(layer_index))\n",
"landsat_image = ee.Image(landsat_images.toList(landsat_images.size()).get(layer_index))\n",
"\n",
"\n",
"left_layer = geemap.ee_tile_layer(water_image, water_vis, 'Water')\n",
"right_layer = geemap.ee_tile_layer(landsat_image, landsat_vis, 'Landsat')\n",
"\n",
"Map = geemap.Map()\n",
"Map.addLayer(landsat_image, landsat_vis, 'Landsat')\n",
"Map.split_map(left_layer, right_layer)\n",
"Map.centerObject(roi, zoom=12)\n",
"Map"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Export results to Google Drive"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"geemap.ee_export_image_collection_to_drive(water_images, folder='export', scale=90)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"geemap.ee_export_image_to_drive(\n",
" occurrence.toInt(),\n",
" description='water_occurrence',\n",
" folder='export',\n",
" region=first_ndwi_image.geometry(),\n",
" scale=90,\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Large-scale surface water mapping"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import ee\n",
"import geemap"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"Map = geemap.Map()\n",
"Map"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"states_shp = geemap.shp_to_ee('../data/us_states.shp')\n",
"Map.addLayer(states_shp, {}, 'US States SHP')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"state_name = 'Nevada'\n",
"roi = ee.FeatureCollection('TIGER/2018/States').filter(ee.Filter.eq('NAME', state_name))\n",
"# roi = states_shp.filterBounds(Map.user_roi)\n",
"Map.addLayer(ee.Image().paint(roi, 0, 2), {'palette': 'red'}, state_name)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"images = geemap.landsat_timeseries(\n",
" roi=roi, start_year=1984, end_year=2019, start_date='06-01', end_date='09-30'\n",
")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"first_image = ee.Image(images.toList(images.size()).get(0))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"landsat_vis = {'bands': ['NIR', 'Red', 'Green'], 'min': 0, 'max': 3500}\n",
"Map.addLayer(first_image, landsat_vis, 'First image')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"layer_names = ['Landsat ' + str(year) for year in range(1984, 2020)]\n",
"Map = geemap.Map()\n",
"Map.ts_inspector(\n",
" left_ts=images,\n",
" right_ts=images,\n",
" left_names=layer_names,\n",
" right_names=layer_names,\n",
" left_vis=landsat_vis,\n",
" right_vis=landsat_vis,\n",
")\n",
"Map.centerObject(roi, zoom=8)\n",
"Map"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"ndwi_threshold = 0\n",
"\n",
"\n",
"def extract_water(img):\n",
" ndwi_image = img.normalizedDifference(['Green', 'NIR'])\n",
" water_image = ndwi_image.gt(ndwi_threshold)\n",
" return water_image"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"water_images = images.map(extract_water)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"Map = geemap.Map()\n",
"\n",
"first_image = ee.Image(images.toList(images.size()).get(0))\n",
"landsat_vis = {'bands': ['NIR', 'Red', 'Green'], 'min': 0, 'max': 3500}\n",
"Map.addLayer(first_image, landsat_vis, 'First image')\n",
"\n",
"first_water_image = ee.Image(water_images.toList(water_images.size()).get(0)).selfMask()\n",
"Map.addLayer(first_water_image, {'palette': 'blue'}, 'First NDWI')\n",
"\n",
"Map"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"layer_index = 0 # Change this index to your desired date\n",
"print(layer_names[layer_index])\n",
"\n",
"water_image = ee.Image(\n",
" water_images.toList(water_images.size()).get(layer_index)\n",
").selfMask()\n",
"landsat_image = ee.Image(images.toList(images.size()).get(layer_index))\n",
"\n",
"water_vis = {'palette': 'blue'}\n",
"\n",
"landsat_vis = {\n",
" 'bands': ['NIR', 'Red', 'Green'],\n",
" 'min': 0,\n",
" 'max': 4000,\n",
" 'gamma': 1.4,\n",
"}\n",
"\n",
"left_layer = geemap.ee_tile_layer(water_image, water_vis, 'Water')\n",
"right_layer = geemap.ee_tile_layer(landsat_image, landsat_vis, 'Landsat')\n",
"\n",
"Map = geemap.Map()\n",
"Map.addLayer(landsat_image, landsat_vis, 'Landsat')\n",
"Map.split_map(left_layer, right_layer)\n",
"Map.centerObject(roi, zoom=12)\n",
"Map"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Use JRC Global Surface Water Product"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import ee\n",
"import geemap"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"Map = geemap.Map()\n",
"Map.add_basemap('HYBRID')\n",
"Map"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"gsw = ee.Image('JRC/GSW1_1/GlobalSurfaceWater')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print(gsw.bandNames().getInfo())"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"occurrence = gsw.select('occurrence')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"vis_occurrence = {'min': 0, 'max': 100, 'palette': ['red', 'blue']}"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"Map.addLayer(occurrence, vis_occurrence, 'Occurrence')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"water_mask = occurrence.gt(90).selfMask()\n",
"vis_water_mask = {'palette': ['white', 'blue']}"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"Map.addLayer(water_mask, vis_water_mask, 'Water Mask (>90%)')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"Map.setCenter(-74.4557, -8.4289, 11)\n",
"Map"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"change = gsw.select(\"change_abs\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"vis_change = {'min': -50, 'max': 50, 'palette': ['red', 'black', 'limegreen']}"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"Map.addLayer(change, vis_change, 'Occurrence change intensity')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"transition = gsw.select('transition')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"Map.addLayer(transition, {}, 'Transition classes')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"label = 'Surface Water Dynamics'\n",
"Map.add_landsat_ts_gif(\n",
" label=label,\n",
" start_year=1985,\n",
" start_date='01-01',\n",
" end_date='12-31',\n",
" bands=['SWIR1', 'NIR', 'Red'],\n",
" font_color='white',\n",
" frames_per_second=10,\n",
" progress_bar_color='blue',\n",
")"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
}
},
"nbformat": 4,
"nbformat_minor": 5
}